Summary of Large Language Models Meet Nlp: a Survey, by Libo Qin et al.
Large Language Models Meet NLP: A Survey
by Libo Qin, Qiguang Chen, Xiachong Feng, Yang Wu, Yongheng Zhang, Yinghui Li, Min Li, Wanxiang Che, Philip S. Yu
First submitted to arxiv on: 21 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study investigates the potential of large language models (LLMs) in Natural Language Processing (NLP) tasks. The researchers explore three questions: how are LLMs currently applied to NLP tasks, have traditional NLP tasks been solved with LLMs, and what is the future of LLMs for NLP? To answer these questions, they provide a comprehensive overview of LLMs in NLP by introducing a unified taxonomy, which includes parameter-frozen application and parameter-tuning application. The study also summarizes new frontiers and associated challenges, aiming to inspire further advancements. This work offers valuable insights into the potential and limitations of LLMs in NLP and serves as a practical guide for building effective LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how powerful language models can be used in natural language processing tasks. The scientists want to know: what are these models currently being used for, have they already solved certain tasks, and where will they go next? To find out, they created a special system that groups different ways of using the models together. They also looked at new areas where these models can be applied and the challenges that come with it. This study helps us understand how well these models work and what we can do to make them even better. |
Keywords
» Artificial intelligence » Natural language processing » Nlp
